Categories for AGI
A course introducing category theory through applications in artificial intelligence, including functors, adjunctions, monoidal structure, causality, and learning.
Teaching and tutorials
The classroom as a research laboratory.
Courses and tutorials have repeatedly served as places to test a new language, discover where an explanation fails, and turn a collection of papers into a coherent line of inquiry.
Knowledge-Enriched Transformers
Tutorials connecting categorical, geometric, and topological structure to modern foundation models.
Optimization for Computer Science
Convexity, duality, first-order methods, proximal algorithms, and their role in machine learning and decision-making.
Deep Learning
Representation learning, neural architectures, optimization, and the evolving foundations of modern machine learning.
Reinforcement Learning and Autonomous Agents
Sequential decisions, value functions, temporal abstraction, partial observability, and multi-agent learning.
Invited lectures and tutorials
A historical collection of talks presented across AI, machine learning, robotics, causality, and category theory.
Course archive
Original materials remain available.
The UMass archive contains lecture notes, slides, assignments, tutorials, and demonstrations from many earlier courses. These are preserved as historical material; the new portal will gradually curate the most enduring resources.
Browse the UMass site ↗